Published Paper Details:

Obese individuals have a high possibility of developing Cardio Vascular Disease (CVD), which should be because of an alteration in the Autonomic Nervous System (ANS) .Heart Rate Variability (HRV) can be utilized to recognize the progressions in ANS movement. An HRV is a device to gauge the ANS action utilizing linear and non- linear HRV features. In this paper, we address the impact of weight on ANS utilizing HRV parameters. At first, two sets of twenty counts are taken; one set contains obesity and one more set is non-obesity regardless of gender, between the ages of 20 to 50. The measurably massive distinction between the two sets was noticed using the Independent t-test. Measurable consequences of the review show the sympathovagal imbalance because of reduced parasympathetic movement. To come up with exact outcomes, we executed deep learning techniques. This paper essentially represents two best profound learning calculations: RNN (Recurrent Neural Network) and LSTM (Long Short-Term Memory). The experimental results show that the LSTM expectation impact is superior to RNN in terms of recognizing CVD.